Route recommendation that assists a user with navigating and interpreting a virtual reality environment

ABSTRACT

Embodiments of the invention are directed to a computer-implemented method of generating a pathway recommendation. The computer-implemented method includes using a processor system to generate an intermediate three-dimensional (3D) virtual reality (VR) environment of a target environment. A machine learning algorithm is used to perform a machine learning task on the intermediate 3D VR environment to generate machine learning task results including predicted features of interest (FOI) and FOI annotations for the intermediate 3D VR environment. The processor system is used to generate, based at least in part on the machine learning task results, the pathway recommendation configured to assist a user with navigating and interpreting a 3D VR environment including the intermediate 3D VR environment having the pathway recommendation.

BACKGROUND

The present invention relates in general to programmable computers. Morespecifically, the present invention relates to computing systems,computer-implemented methods, and computer program products configuredand arranged to generate personalized route recommendations that assista person with navigating and interpreting a three-dimensional (3D)virtual reality (VR) environment in order to perform a task.

An immersive VR system is a computer system configured to generateimmersive three-dimensional (3D) VR environments that model acorresponding actual environment. The immersive 3D VR environment isconfigured and arranged to deliver sensory impressions to the humansenses (sight, sound, touch, smell, and the like) that mimic the sensoryimpressions that would be delivered to the human senses by thecorresponding actual environment. The basic components of a VR systemcan include a head-mounted device (HMD) worn by a user; a computingsystem; and various feedback components that provide inputs from theuser to the computing system. The type and quality of these sensoryimpressions determine the level of immersion and the feeling of presencein the VR system. The HMD displays to the user immersive visualinformation within the user's field of view. Other outputs provided bythe HMD can include audio output and/or haptic feedback. The user canfurther interact with the HMD by providing inputs for processing by oneor more components of the HMD. For example, the user can provide tactileinputs, voice commands, and other inputs while the HMD is mounted to theuser's head. Examples of actual environments that can be modeled by animmersive 3D VR environment include a human; an animal; invertebrates; asubterranean environment; and/or an aqueous environment such as anocean, a lake, a stream, and/or a pond.

SUMMARY

Embodiments of the invention are directed to a computer-implementedmethod of generating a pathway recommendation. The computer-implementedmethod includes using a processor system to generate an intermediatethree-dimensional (3D) virtual reality (VR) environment of a targetenvironment. A machine learning algorithm is used to perform a machinelearning task on the intermediate 3D VR environment to generate machinelearning task results including predicted features of interest (FOI) andFOI annotations for the intermediate 3D VR environment. The processorsystem is used to generate, based at least in part on the machinelearning task results, the pathway recommendation configured to assist auser with navigating and interpreting a 3D VR environment including theintermediate 3D VR environment having the pathway recommendation.

Embodiments of the invention are also directed to computer systems andcomputer program products having substantially the same features as thecomputer-implemented method described above.

Additional features and advantages are realized through techniquesdescribed herein. Other embodiments and aspects are described in detailherein. For a better understanding, refer to the description and to thedrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter which is regarded as embodiments is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The foregoing and other features and advantages ofthe embodiments are apparent from the following detailed descriptiontaken in conjunction with the accompanying drawings in which:

FIG. 1 depicts a block diagram illustrating a system according toembodiments of the invention;

FIG. 2 depicts a block diagram illustrating a system according toembodiments of the invention;

FIG. 3A depicts a block diagram illustrating an example of athree-dimensional (3D) virtual reality (VR) environment having one ormore pathway recommendations according to embodiments of the invention;

FIG. 3B depicts a block diagram illustrating an example of a 3D VRenvironment having a pathway recommendation according to embodiments ofthe invention;

FIG. 3C depicts a block diagram illustrating a computing system havingmachine learning algorithms and pathway recommender modules according toembodiments of the invention;

FIG. 4 depicts a block diagram illustrating a display of a head-mounteddevice (HDM) according to embodiments of the invention;

FIG. 5 depicts a flow diagram illustrating a computer-implementedmethodology according to embodiments of the invention;

FIG. 6 depicts a block diagram illustrating a system according toembodiments of the invention;

FIG. 7 depicts a flow diagram illustrating a computer-implementedmethodology according to embodiments of the invention;

FIG. 8A depicts a block diagram illustrating results of operationsperformed by the methodology shown in FIG. 7 ;

FIG. 8B depicts a block diagram illustrating results of operationsperformed by the methodology shown in FIG. 7 ;

FIG. 8C depicts a block diagram illustrating results of operationsperformed by the methodology shown in FIG. 7 ;

FIG. 8D depicts a block diagram illustrating results of operationsperformed by the methodology shown in FIG. 7 ;

FIG. 8E depicts a block diagram illustrating results of operationsperformed by the methodology shown in FIG. 7 ;

FIG. 9 depicts a machine learning system that can be utilized toimplement aspects of the invention;

FIG. 10 depicts a learning phase that can be implemented by the machinelearning system shown in FIG. 9 ; and

FIG. 11 depicts details of an exemplary computing system capable ofimplementing various aspects of the invention.

In the accompanying figures and following detailed description of thedisclosed embodiments, the various elements illustrated in the figuresare provided with three digit reference numbers. In some instances, theleftmost digits of each reference number corresponds to the figure inwhich its element is first illustrated.

DETAILED DESCRIPTION

For the sake of brevity, conventional techniques related to making andusing aspects of the invention may or may not be described in detailherein. In particular, various aspects of computing systems and specificcomputer programs to implement the various technical features describedherein are well known. Accordingly, in the interest of brevity, manyconventional implementation details are only mentioned briefly herein orare omitted entirely without providing the well-known system and/orprocess details.

As used herein, the terms “immersive virtual reality” system andvariations thereof are intended to define a computer system thatdelivers sensory impressions to the human senses (sight, sound, touch,smell, and the like) that mimic the three-dimensional (3D) sensoryimpressions that would be delivered to the human senses by an actual 3Denvironment. The basic components of an immersive VR system can includea head-mounted device (HMD) worn by a user; a computing system; andvarious feedback components that provide inputs from the user to thecomputing system.

As used herein, in the context of machine learning algorithms, the terms“input data” and variations thereof are intended to cover any type ofdata or other information that is received at and used by the machinelearning algorithm to perform training, learning, classificationoperations, and/or tasks.

As used herein, in the context of machine learning algorithms, the terms“training data” and variations thereof are intended to cover any type ofdata or other information that is received at and used by the machinelearning algorithm to perform training and/or learning operations.

As used herein, in the context of machine learning algorithms, the terms“application data,” “real world data,” “actual data,” and variationsthereof are intended to cover any type of data or other information thatis received at and used by the machine learning algorithm to performclassification operations and/or tasks.

Many of the functional units of the systems described in thisspecification have been labeled as modules. Embodiments of the inventionapply to a wide variety of module implementations. For example, a modulecan be implemented as a hardware circuit including custom VLSI circuitsor gate arrays, off-the-shelf semiconductors such as logic chips,transistors, or other discrete components. A module can also beimplemented in programmable hardware devices such as field programmablegate arrays, programmable array logic, programmable logic devices or thelike. Modules can also be implemented in software for execution byvarious types of processors. An identified module of executable codecan, for instance, include one or more physical or logical blocks ofcomputer instructions which can, for instance, be organized as anobject, procedure, or function. Nevertheless, the executables of anidentified module need not be physically located together, but caninclude disparate instructions stored in different locations which, whenjoined logically together, function as the module and achieve the statedpurpose for the module.

Many of the functional units of the systems described in thisspecification have been labeled as models. Embodiments of the inventionapply to a wide variety of model implementations. For example, themodels described herein can be implemented by machine learningalgorithms and natural language processing algorithms configured andarranged to uncover unknown relationships between data/information andgenerate a model that applies the uncovered relationship to newdata/information in order to perform an assigned task of the model. Inaspects of the invention, the models described herein can have all ofthe features and functionality of the models depicted in FIGS. 9 and 10and described in greater detail subsequently herein.

The various components, modules, models, algorithms, and the like of thesystems illustrated herein are depicted separately for ease ofillustration and explanation. In embodiments of the invention, thefunctions performed by the various components, modules, models,algorithms, and the like of the systems illustrated herein can bedistributed differently than shown without departing from the scope ofthe embodiments of the invention unless it is specifically statedotherwise.

Turning now to an overview of technologies that are relevant to aspectsof the invention, computer models of actual systems or environments canbe used to provide a convenient way of analyzing aspects of the actualsystem/environment by analyzing the computer model of the actualsystem/environment. One area where computer models can be useful is inseismic interpretation of subterranean regions. Geoscientists useseismic surveys, which are “ultra-sound” images of the underground, tolook for geological features that can be evidences to their theoriesabout the subterranean region. The task of interpreting seismic surveysand other seismic information is challenging, cognitively intensive, andtime consuming for a variety of reasons. For example, the expectedgeological features can be completely unknown; the data is a 3D volumewhich may contains billion of voxels and can cover thousands ofkilometers; the interpretation can take from months to years; and thespatial-relationship between the features is key to understanding thesubterranean region.

Turning now to an overview of aspects of the invention, embodiments ofthe invention provide computing systems, computer-implemented methods,and computer program products configured and arranged to generatepersonalized route recommendations that are integrated within athree-dimensional (3D) virtual reality (VR) environment of a targetenvironment, and that assist a user with navigating and interpreting the3D VR environment of the target environment in order to perform a usertask. In embodiments of the invention, a machine learning algorithm istrained to perform the task of predicting features of interest (FOI) andexplanatory annotations of the FOI for a 3D VR environment. The systemtakes the predicted FOI and the FOI annotations generated by the machinelearning algorithms, along with the user task and user profile data, andgenerates a novel 3D VR environment having the personalized routerecommendations that assist the user with navigating and interpretingthe novel 3D VR environment in order to perform the user task.

In embodiments of the invention, the machine learning algorithms can betrained by using a historical target environment analysis corpus and/oruser feedback data. In embodiments of the invention, the historicaltarget environment analysis corpus can include a wide variety ofinformation from relevant prior analyses performed by trainedinterpreters (e.g., a geological scientist) on relevant prior targetenvironments in order to complete one or more prior user tasks,including, for example, the task of finding underground reservoirs ofoil or natural gas within the prior target environments and/orsupporting already-discovered underground reservoirs of oil or naturalgas within the prior target environments. For example, in someembodiments of the invention, the historical target environment analysiscorpus can include recorded data and analyses generated by a trainedinterpreter such as seismic measurements; graphs; charts; written notes;written reports; descriptions of the supporting tools and techniquesused in the prior analyses; explanations of successful analysistechniques; explanations of unsuccessful analysis techniques;explanations of geological patterns that contributed to successfullycompleting the task; explanations of geological patterns that did notcontribute to successfully completing the task; and the like.

In embodiments of the invention, the user feedback data is feedback thesystem receives from the user about the pathway recommendation(s) whenthe user is being guided through the novel 3D VR environment by thepathway recommendation(s). In accordance with aspects of the invention,the user feedback data includes information that conveys how well agiven pathway recommendation is performing. For example, the userfeedback data can include the user's agreement and/or disagreement withsome aspect of the pathway recommendation(s), along with the user'srationale for the agreement and/or disagreement. In some embodiments ofthe invention, the user's rationale for the agreement and/ordisagreement with some aspect of the pathway recommendation(s) can be inthe form of spoken natural language, and the novel 3D VR environment caninclude suitable voice-to-text circuitry to convert the user's spokennatural language feedback to text that functions at the user feedbackdata.

Turning now to a more detailed description of the aspects of the presentinvention, FIG. 1 depicts a diagram illustrating an immersive virtualreality (VR) system 100 according to embodiments of the invention. Inembodiments of the invention, the system 100 includes a programmablecomputer 110, a transducer system 112, an HMD 150, and a manipulationdevice 152 (e.g., a three-dimensional mouse, data gloves, etc.),configured and arranged as shown. The HMD 150 is configured to be wornby a user 140, and the manipulation device(s) 152 are configured to beworn by and/or otherwise controlled/used by the user 140. The computingsystem 110 is in wired and/or wireless communication with the transducersystem 112 and the HMD 150. The HMD 150 is in wired and/or wirelesscommunication with the manipulation device(s) 152.

In aspects of the invention, the transducer system 112 can be positionedon, above, and/or below the target environment surface 132 andconfigured to send any one of a variety of forms of acoustic energy intoa target environment 130. The target environment 130 can be anyenvironment that is partially transparent to acoustic signals and ableto reflect at least some of the acoustic signals back to the transducersystem 112. In accordance with aspects of the invention, the targetenvironment 130 can include but is not limited to a human; an animal;invertebrates; a subterranean environment; and/or an aqueous environmentsuch as an ocean, a lake, a stream, and/or a pond. In some embodimentsof the invention, the transducer system 112 can be a network ofindividual transducers. In some embodiments of the invention, eachindividual transducer system 112 in the network can be configured toboth transmit and receive acoustic signals. In some embodiments of theinvention, each individual transducer system 112 in the network can beimplemented as a separate transmission component and a separate receivercomponent. In some embodiments of the invention, the acoustic signalstransmitted and/or received by the transducer system 112 can be any typeof acoustic signal including, for example, ultrasonic acoustic signalsand/or seismic acoustic signals.

The acoustic energy transmitted into the target environment 130 isreflected from the various elements that make up the target environment130 and returned to the transducer system 112 as reflected acousticenergy 114. The transducer system 112 captures the reflected acousticenergy 114 and provides it to the computing system 110. The computingsystem 110 executes software algorithms configured and arranged to usethe acoustic signals 114 and an input data stream 116 received from theHMD 150 to generate the output data stream 118 and provide it to the HMD150. In accordance with embodiments of the invention, the output datastream 118 includes a three-dimensional (3D) VR environment 120 that isa computer-generated 3D model of the target environment 130. Immersive(i.e., 3D) views of the 3D VR environment 120 can be displayed to theuser 140 on a display (e.g., display 206 shown in FIG. 2 ) of the HMD150, which places tiny screens and lenses close to the user's eyes tosimulate large screens that encompass most of the user's field of view.As the user 140 performs actions like walking, head rotating (i.e.,changing the point of view), data describing behavior of the user 140 isfed through the input data stream 116 to the computing system 110 fromthe HMD 150 and/or the manipulation devices 152. The computing system110 processes the information in real-time and generates appropriatefeedback that is passed back to the user 140 by means of the output datastream 118.

In accordance with aspects of the invention, algorithms of the computingsystem 110 are configured to generate the personalized pathwayrecommendation(s) 122 and integrate the same with the 3D VR environment120. In accordance with aspects of the invention, the pathwayrecommendation(s) 122 function as a guide for assisting the user 140with traversing the 3D VR environment 120 in a manner that assists theuser 140 with visualizing and interpreting important features of the 3DVR environment 120 in order to perform a particular user task. Inembodiments of the invention, the user 140 can be an expert on someaspect of the target environment 130, and the user task can be gaining abetter understanding of the features of the target environment 130. Inaccordance with aspects of the invention, the computing system 110 canbe configured to include machine learning algorithms that analyze the 3DVR environment 120 to identify the points of interest in the environment130 that are relevant to the user task. In some embodiments of theinvention, the pathway recommendation(s) 122 are personalized for theuser 140 and for the task being performed by the user 140. Inembodiments of the invention, the pathway recommendation(s) 122 canfurther include selections of the appropriate scale and orientation ofeach point of interest that are appropriate for the user 140 and theuser task. Embodiments of the invention generate the pathwayrecommendations 122 and track/store what the user 140 views whilenavigating the pathway recommendations 122 through the 3D VR environment120, thereby providing a provenance of data that can be used by thecomputing system 110 to dynamically update the pathwayrecommendation(s), thereby providing dynamically updated insights andannotations to the user 140. Additional details of how the computingsystem 110 can generate the 3D VR environment 120 and the pathwayrecommendation(s) 122 in accordance with aspects of the invention aredepicted in FIGS. 3C and 5-11 and described in greater detailsubsequently herein.

FIG. 2 depicts an HMD 150A, which is a non-limiting example of how theHMD 150 (shown in FIG. 1 ) can be implemented. In accordance withaspects of the invention, the HMD 150A includes control circuitry 202and input-output circuitry 204, configured and arranged as shown. Theinput-output circuitry 204 includes displays 206, optical components208, input-output devices 210, and communications circuitry 218,configured and arranged as shown. The input-output devices 210 includesensors 212 and audio components 214, configured and arranged as shown.The various components of the HMD 150A can be supported by ahead-mountable support structure such as a pair of glasses; a helmet; apair of goggles; and/or other head-mountable support structureconfigurations.

In embodiments of the invention, the control circuitry 202 can includestorage and processing circuitry for controlling the operation of theHMD 150A. The control circuitry 202 can include storage such as harddisk drive storage, nonvolatile memory (e.g.,electrically-programmable-read-only memory configured to form a solidstate drive), volatile memory (e.g., static or dynamicrandom-access-memory), etc. Processing circuitry in the controlcircuitry 202 can be based on one or more microprocessors,microcontrollers, digital signal processors, baseband processors, powermanagement units, audio chips, graphic processing units, applicationspecific integrated circuits, and other integrated circuits. Computerprogram instructions can be stored on storage in the control circuitry202 and run on processing circuitry in the control circuitry 202 toimplement operations for HMD 150A (e.g., data gathering operations,operations involving the adjustment of components using control signals,image rendering operations to produce image content to be displayed fora user, etc.).

The input-output circuitry 204 can be used to allow the HMD 150A toreceive data from external equipment (e.g., the computing system 110(shown in FIG. 1 ); the transducer system 112; a portable device such asa handheld device; a laptop computer; or other electrical equipment) andto allow the user 140 (shown in FIG. 1 ) to provide the HMD 150A withuser input. The input-output circuitry 204 can also be used to gatherinformation on the environment in which HMD 150A is operating. Outputcomponents in the input-output circuitry 204 can allow the HMD 150A toprovide the user 140 with output and can be used to communicate withexternal electrical equipment.

Display(s) 206 of the input-output circuitry 204 can be used to displayimages (e.g., the 3D VR environment 120 (shown in FIG. 1 )) to the user140 (shown in FIG. 1 ) of the HMD 150A. The display(s) 206 can beconfigured to have pixel array(s) to generate images that are presentedto the user 140 through an optical system. The optical system can, ifdesired, have a transparent portion through which the user 140 (viewer)can observe real-world objects while computer-generated content isoverlaid on top of the real-world objects by producingcomputer-generated images (e.g., the 3D VR environment 120) on thedisplay(s) 206. In embodiments of the invention, the display(s) 206 areimmersive views of the 3D VR environment 120, wherein the display(s) 206place tiny screens and lenses close to the user's eyes to simulate largescreens that encompass most of the user's field of view. As the user 140performs actions like walking, head rotating (i.e., changing the pointof view), data describing behavior of the user 140 (shown in FIG. 1 ) isfed to the computing system 110 (shown in FIG. 1 ) from the HMD 150Aand/or the manipulation devices 152 (shown in FIG. 1 ).

The optical components 208 can be used in forming the optical systemthat presents images to the user 140. The optical components 208 caninclude static components such as waveguides, static optical couplers,and fixed lenses. The optical components 208 can also include adjustableoptical components such as an adjustable polarizer, tunable lenses(e.g., liquid crystal tunable lenses; tunable lenses based onelectro-optic materials; tunable liquid lenses; microelectromechanicalsystems (MLMS) tunable lenses; or other tunable lenses), a dynamicallyadjustable coupler, and other optical devices formed fromelectro-optical materials (e.g., lithium niobate or other materialsexhibiting the electro-optic effect). The optical components 208 can beused in receiving and modifying light (images) from the display 206 andin providing images (e.g., the 3D VR environment 120) to the user 140for viewing. In some embodiments of the invention, one or more of theoptical components 208 can be stacked so that light passes throughmultiple of the components 208 in series. In embodiments of theinvention, the optical components 208 can be spread out laterally (e.g.,multiple displays can be arranged on a waveguide or set of waveguidesusing a tiled set of laterally adjacent couplers). In some embodimentsof the invention, both tiling and stacking configurations are present.

The input-output devices 210 of the input-output circuitry 204 areconfigured to gather data and user input and for supplying the user 140(shown in FIG. 1 ) with output. The input-output devices 210 can includesensors 212, audio components 214, and other components for gatheringinput from the user 140 and/or or the environment surrounding the HMD150A and for providing output to the user 140. The input-output devices210 can, for example, include keyboards; buttons; joysticks; touchsensors for trackpads and other touch sensitive input devices; cameras;light-emitting diodes; and/or other input-output components. Forexample, cameras or other devices in the input-output circuitry 204 canface the eyes of the user 140 and track the gaze of the user 140. Thesensors 212 can include position and motion sensors, which can include,for example, compasses; gyroscopes; accelerometers and/or other devicesfor monitoring the location, orientation, and movement of the HDM 150A;and satellite navigation system circuitry such as Global PositioningSystem (GPS) circuitry for monitoring location of the user 140. Usingthe sensors 212, for example, the control circuitry 202 can monitor thecurrent direction in which a user's head is oriented relative to thesurrounding environment. Movements of the user's head (e.g., motion tothe left and/or right to track on-screen objects and/or to viewadditional real-world objects) can also be monitored using the sensors212.

In some embodiments of the invention, the sensors 212 can includeambient light sensors that measure ambient light intensity and/orambient light color; force sensors; temperature sensors; touch sensors;capacitive proximity sensors; light-based proximity sensors; other typesof proximity sensors; strain gauges; gas sensors; pressure sensors;moisture sensors; magnetic sensors; and the like. The audio components214 can include microphones for gathering voice commands and other audioinput and speakers for providing audio output (e.g., ear buds, boneconduction speakers, or other speakers for providing sound to the leftand right ears of a user). In some embodiments of the invention, theinput-output devices 210 can include haptic output devices (e.g.,vibrating components); light-emitting diodes and other light sources;and other output components. The input-output circuitry 204 can includewired and/or wireless communications circuitry 216 that allows the HMD150A (e.g., using the control circuitry 202) to communicate withexternal equipment (e.g., remote controls, joysticks, input controllers,portable electronic devices, computers, displays, and the like) and thatallows signals to be conveyed between components (circuitry) atdifferent locations in the HMD 150A.

FIG. 3A depicts an example of a novel immersive 3D VR environment 120Ahaving pathway recommendations 122A in accordance with embodiments ofthe invention. The immersive 3D VR environment 120A is an example of howthe 3D VR environment 120 (shown in FIG. 1 ) can be implemented, and thepathway recommendation(s) 120A are an example of how the pathwayrecommendation(s) 120 (shown in FIG. 1 ) can be implemented. Inaccordance with embodiments of the invention, the immersive 3D VRenvironment 120A is a virtual reality representation of the targetenvironment 130 where the target environment 130 is a subterraneanregion, and where the task being performed by the user 140 is any one ofa variety of different analyses and sub-analyses of the 3D VRenvironment 120A as a proxy for the target environment 130. Thedifferent analyses and sub-analyses of the 3D VR environment 120A relateto finding underground reservoirs of oil or natural gas in the targetenvironment 130, and/or relate to supporting already-discoveredunderground reservoirs of oil or natural gas in the target environment130.

The reflected acoustic signals 114 (shown in FIG. 1 ) used by thecomputing system 110 (shown in FIG. 1 ) to generate the immersive 3D VRenvironment 120A can be seismic waves, which are the same type of wavesused to study earthquakes. In general, seismic waves of energy movethrough the earth just as sound waves move through the air. In oil andgas exploration, the transmission elements of multiple instances of thetransducer systems 112 are spaced apart and configured to send seismicwaves deep into the earth (i.e., the target environment 130). Theseismic waves that bounce back (i.e., the reflected acoustic signals114) are recorded by receiver elements of the transducer systems 112.This reflection process is known generally as reflection seismology. Thereflected seismic waves are used by the computing system 110 to image aselected region (e.g., a 100 kilometer by 100 kilometer square) of theinterior of the earth to generate the 3D VR environment 120A, which canbe analyzed by the user 140. In the embodiments of the inventiondepicted in FIG. 3A, the user 140 is an expert interpreter trained toperform one or more types of analysis and interpretation tasks. Forexample, the tasks performed by the user 140 can be any one of a varietyof different analyses and sub-analyses of the 3D VR environment 120Athat relate to finding underground reservoirs of oil or natural gaswithin the target environment 130 and/or supporting already-discoveredunderground reservoirs of oil or natural gas within the targetenvironment 130.

In accordance with aspects of the invention, the pathway recommendation122A includes points of interest 124A, point-of-interest (POI)connectors 126A, pathway guidance 128A, and POI patterns 130A,configured and arranged as shown. In embodiments of the invention, thePOI patterns 130A can be identified to the user 140 through the pathwayguidance 128A. In aspects of the invention, the points of interest 124Acan be implemented as multiple so-called “slices” of the immersive 3D VRenvironment 120A. In aspects of the invention, each slice is controlledto present to the user 140 a particular view (e.g., zoom-level;scale/size; shape/contour; orientation with respect to the user 140; andthe like) of a portion of the 3D VR environment 120A that the computingsystem 110 has determined is an area of interest 124A to the overalltask being performed by the user 140. In some embodiments of theinvention, the points of interest 124A are two-dimensional (2D) “sliced”views highlighted within a 3D immersive view of the immersive 3D VRenvironment 120A being shown to the user 140 through a display 206 ofthe HMD 150A. In accordance with aspects of the invention, the points ofinterest 124A can be generated by machine learning algorithms (e.g.,machine learning algorithm 320 shown in FIG. 3C; and/or classifier 910shown in FIG. 9 ) trained to perform a machine learning (ML) task 322(shown in FIG. 3C) on an intermediate 3D VR environment 318 (shown inFIG. 3C). Details of how the computing system 110 can utilize machinelearning algorithms 320 to generate the points of interest 124A aredepicted in FIG. 3C and described in greater detail subsequently herein.

The POI connectors 126A are various pathway segments that will bevirtually traversed by the user 140 when moving from one instance of thepoints of interest 124A to another instance of the points of interest124A under guidance received from the pathway guidance 128A of thepathway recommendations 122A. In embodiments of the invention, thecomputing system 110 can be configured to conduct an “acceleratedtraverse” of the user 140 through the POI connectors 126A by, uponreceiving an appropriate command from the user 140 (e.g., through themanipulation device(s) 152 shown in FIG. 1 ), automatically moving theuser 140 from one instance of the points of interest 124A through thePOI connectors 126A to another instance of the points of interest 124Awithout requiring actual movement by the user 140; by requiring onlyminimal movement by the user 140; and/or by skipping the POI connectors126A altogether when moving the user 140 from one instance of the pointsof interest 124A to another instance of the points of interest 124A.

The pathway guidance 128A includes various forms of communication(natural language text; natural language audio; visual cues; zoom-leveladjustments; scale adjustments; adjustments to the orientation of thepoint of interest 124A with respect to the user 140; and the like)generated by the computing system 110 and provided through the HMD 150,150A to the user 140 to actively guide the user 140 through the POIconnectors 126A and/or the points of interest 124A. In embodiments ofthe invention, the pathway recommendation 122A, and more specificallythe pathway guidance 128A, functions as recommendations/steps that theuser 140 can follow, including for example hints on how to conduct an“accelerated traverse” of the 3D VR environment 120A using the shortestpath(s).

In embodiments of the invention, the various forms of communication(natural language text; natural language audio; visual cues; zoom-leveladjustments; scale adjustments; adjustments to the orientation of thepoint of interest 124A with respect to the user 140; and the like)generated by the pathway guidance 128A can communicate with the user 140in a way that provides context for each instance of the points ofinterest 124A. In general, an individual instance of the points ofinterest 124A (i.e., each individual “slice” of the 3D VR environment120) conveys limited information. However, it is the points of interest124A that precede and/or follow a given point of interest 124A that canconvey information relevant to the user task. The POI pattern 130A isone example of a meaningful grouping of the points of interest 124A,where the meaningful grouping conveys information that can be relevantto the user task. In embodiments of the invention, the various forms ofcommunication provided by the pathway guidance 128A highlights to theuser 140 that the points of interest 124A in the POI pattern 130A arerelated, and further provides to the user 140 an explanation of why thepoints of interest 124A in the POI pattern 130A are related, as well asan explanation of how the points of interest 124A in the POI pattern130A are relevant to the user task. In some embodiments of theinvention, the points of interest 124A in the POI pattern 130A can besequential, non-sequential, and/or combinations thereof. In embodimentsof the invention, the pathway guidance 128A can be configured toencourage the user 140 to traverse a given POI pattern(s) 130A multipletimes until the user 140 provides feedback to the computing system 110confirming that the user 140 sees and understands the meaning that thepathway guidance 128A asserts is contained within the POI pattern(s)130A. In embodiments of the invention, a given POI pattern 130A can haveany number of points of interest 124A, and the pathway guidance 128A canidentify meaning conveyed within one POI pattern 130A, as well asmeaning conveyed by groupings of separate instances of the POI pattern130A.

In embodiments of the invention, the user 140 can be an expert trainedto analyze the target environment 130 for the purpose of performing avariety of user tasks. In some aspects of the invention, the computingsystem 110 can be configured to pull the personalized information of theuser 140 from a stored profile of the user 140. The pathwayrecommendation(s) 122A generated by the computing system 110 can takeinto account personalized information of the user 140, which allowsvarious aspect of the pathway recommendation(s) 122, 122A to bepersonalized for the particular user 140. For example, if the storedprofile of the user 140 indicates that the user 140 is a novice atanalyzing the target environment 130, the computing system 110 can beconfigured and arranged to generate a pathway recommendation 122A thatrecommend paths through the 3D VR environment 120A that are appropriatefor a novice at analyzing the target environment 130. If the storedprofile of the user 140 indicates that the user 140 is trained to focuson analyzing particular subset features of the target environment 130,the computing system 110 can be configured and arranged to generate apathway recommendation 122A that recommends paths through the 3D VRenvironment 120A that is appropriate for analyzing the particular subsetfeatures of the target environment 130. If the stored profile of theuser 140 indicates that the user 140 is trained to focus on analyzingbroader and more holistic features of the target environment 130, thecomputing system 110 can be configured and arranged to generate pathwayrecommendation(s) 122A that recommend paths through the 3D VRenvironment 120A that are appropriate for analyzing broader and moreholistic features of the target environment 130. In some embodiments ofthe invention, the stored personalized information of the user 140 canbe entered into the computing system 110 by the user 140.

FIG. 3B depicts another block diagram of the immersive 3D VR environment120A having a pathway recommendation 122A formed from points of interest124A, POI connectors 126A, pathway guidance 128A, and POI patterns 130Ain accordance with embodiments of the invention. The immersive 3D VRenvironment 120A shown in FIG. 3B is substantially the same as theimmersive 3D VR environment 120A shown in FIG. 3A except that in FIG. 3Bmultiple positions of the user 140 are shown, which depicts how the user140 can be guided by the pathway guidance 128A to traverses the multipleconnectors 126A and the multiple points of interest 124A of the pathwayrecommendation 122A. FIG. 3B better illustrates how each of the multipleinstances of the points of interest 124A can be controlled, inaccordance with aspects of the invention, to present to the user 140 aparticular view (e.g., zoom-level; scale/size; shape/contour;orientation with respect to the user 140; and the like) that is selectedby the computing system 110 to assist with the task being performed bythe user 140. For example, the instances of the points of interest 124Ashown in FIG. 3B have various sizes and various orientations withrespect to the user 140. As previously noted herein, the tasks performedby the user 140 can be any one of a variety of different analyses andsub-analyses of the 3D VR environment 120A that relate to findingunderground reservoirs of oil or natural gas within the targetenvironment 130 and/or supporting already-discovered undergroundreservoirs of oil or natural gas within the target environment 130.

FIG. 3C depicts a block diagram illustrating a supporting system 302that can be used to generate the 3D VR environment 120, 120A the pathwayrecommendation(s) 120, 122A in accordance with aspects of the invention.The system 302 includes the computing system 110 communicatively coupledto a network 304. The network 304 is configured to receive and couple tothe computing system 110 various inputs including user inquiries (ortasks) 314, seismic analysis data 312, and training data 306. Inembodiments of the invention, the seismic analysis data 312 includes anyand all data that is required by the computing system 110 in order togenerate the intermediate 3D VR environment 318, including but notlimited to seismic data gathered by the transducer system 112 (shown inFIG. 1 ). The 3D VR environment 318 is intermediate in that theenvironment 318 does not yet include the pathway recommendations 122,122A. The user inquiry/task 314 identifies details of a user task to beperformed by the user 140. As previously noted herein, the tasksperformed by the user 140 can be any one of a variety of differentanalyses that relate to finding underground reservoirs of oil or naturalgas within the target environment 130, and/or supportingalready-discovered underground reservoirs of oil or natural gas withinthe target environment 130. The user inquiry/task 314 can also includeprofile information of the user 140.

The computing system 110 includes machine learning algorithms 320 thathave been trained to perform a machine learning (ML) task 322 on theintermediate 3D VR environment 318. In accordance with aspects of theinvention, the ML task 322 generates ML task results 324 that includepredicted features of interest (FOI) within the intermediate 3D VRenvironment 318, as well as predicted explanatory annotations of thepredicted FOI. In accordance with aspects of the invention, the FOI arethe features of the intermediate 3D VR environment 318 that are relevantto and/or support a successful completion of the user inquiry/task 314.In accordance with aspects of the invention, the explanatory annotationsinclude but are not limited to explanations of why the predicted FOI ofthe intermediate 3D VR environment 318 are related to one another, aswell as an explanation of how the predicted FOI of the intermediate 3DVR environment 318 are relevant to the user task. In accordance withaspects of the invention, the FOI, the explanatory annotations of theFOI, and other information (e.g., stored user profile information) areused by a pathway recommender module 326 of the computing system 110 to,in effect, solve an optimization problem that outputs the points ofinterest 124A, the pathway guidance 128A, and the POI pattern(s) 130A,all of which are used by the computing system 110 to generate thepathway recommendation 122, 122A; combine the pathway recommendation122, 122A with the intermediate 3D VR environment 318; and output the 3DVR environment 120, 120A to the HMD 150, 150A. In embodiments of theinvention, the pathway recommender module 326 is configured to includean optimization algorithm that executed iteratively by comparing varioussolutions until an optimum or a satisfactory solution is found.

In accordance with aspects of the invention, the machine learningalgorithms 320 can be trained to perform the ML task 322 using thetraining data 306, which can include a historical target environmentanalysis corpus 308 and/or user feedback data 310. In aspects of theinvention, the historical target environment analysis corpus 308 caninclude a wide variety of information from relevant prior analysesperformed by trained interpreters (e.g., a geological scientist) onrelevant prior target environments in order to complete one or moreprior user tasks, including, for example, the task of findingunderground reservoirs of oil or natural gas within the prior targetenvironments and/or supporting already-discovered underground reservoirsof oil or natural gas within the prior target environments. For example,in some embodiments of the invention, the historical target environmentanalysis corpus 308 can include recorded data and analyses (seismicmeasurements; graphs; charts; written notes; written reports;descriptions of the supporting tools and techniques used in the prioranalyses; explanations of successful analysis techniques; explanationsof unsuccessful analysis techniques; explanations of geological patternsthat contributed to successfully completing the task; explanations ofgeological patterns that did not contribute to successfully completingthe task; and the like) generated by the trained interpreters (e.g., ageological scientist) who successfully completed the one or more prioruser tasks. In some embodiments of the invention, the historical targetenvironment analysis corpus 308 can include recorded data and analysis(seismic measurements; graphs; charts; written notes; written reports;descriptions of the supporting tools and techniques used in the prioranalyses; explanations of successful analysis techniques; explanationsof unsuccessful analysis techniques; explanations of geological patternsthat did not contribute to successfully completing the task; and thelike) generated by trained interpreters (e.g., a geological scientist)who did not successfully complete the one or more prior user tasks.

In embodiments of the invention, the user feedback data 310 is feedbackthe system 100 receives from the user 140 about the pathwayrecommendation(s) 122, 122A when the user 140 is being guided throughthe 3D VR environment 120, 120A by the pathway recommendation(s) 122,122A. In accordance with aspects of the invention, the user feedbackdata 310 includes information that conveys how well a given pathwayrecommendation 122A is performing. For example, the user feedback data310 can include the user's agreement and/or disagreement with someaspect of the pathway recommendation(s) 122, 122A, including but notlimited to the pathway guidance 128A and/or the POI pattern(s) 130A. Insome embodiments of the invention, the user feedback data 310 caninclude the user's observations (e.g., time spent on a POI, zoomingin/out on POI) about the 3D VR environment 120, 122A that are not partof the pathway recommendation(s) 122, 122A but that the user 140believes is or may be relevant to the user inquiry/task 314 based on theuser's observations while traversing the 3D VR environment 120, 120A.The machine learning algorithms 320 can utilize the user feedback data310 to assess the accuracy of the pathway recommendation 122A that iscurrently being provide to the user 140; further train the machinelearning algorithms 320 and the ML task 322; and dynamically adjust anyaspect of the pathway recommendation(s) 122, 122A.

In aspects of the invention, the system 302 can be configured andarranged to have the features and functionality of the machine learningor classifier system 900 depicted in FIG. 9 . Additional details of howthe system 302 can be utilized to perform the ML task 322 are depictedin FIGS. 6-8E and described in greater detail subsequently herein.Additional details of how the computing system 110 uses the ML results324 to form the points of interest 124A, the POI connectors 126A, thepathway guidance 128A, and the POI guidance 130A of the pathwayrecommendation 122, 122A are depicted in FIGS. 6-8E and described ingreater detail subsequently herein.

FIG. 4 depicts a display 400, which is an example of how the display 206of the HMD 150A can be implemented in accordance with aspects of theinvention. The display 400 is configured to provide visual sensoryfeedback configured and arranged to convey the 3D VR environment 120,120A to the user 140 in accordance with embodiments of the invention. Asshown, the display 400 can be segmented into various regions including ahighlighted point of interest 402 (which corresponds to one of themultiple instances of the points of interest 124A (shown in FIGS. 3A and3B)); a visualization of the non-highlighted seismic data in the 3D VRenvironment 120, 120A; and an indicator 404 (e.g., an arrow) that guidesthe user 140 along the pathway recommendation 120, 122A to the nextinstance of the highlighted point of interest 402 (which corresponds toone of the multiple instances of the points of interest 124A (shown inFIGS. 3A and 3B)). In some embodiments of the invention, the pathwayrecommendation(s) 122, 122A are personalized for the particular user 140and the particular user task (e.g., the user inquiry/task 314) beingperformed by the user 140. In embodiments of the invention, the pathwayrecommendation(s) 122, 122A further include selection of the appropriatescale and orientation of each highlighted point of interest 402 that isappropriate for the user 140 and the task.

In embodiments of the invention, the display 400 of the HMD 150, 150A isconfigured and arranged to track and store what the user 140 views whilenavigating the pathway recommendations 122, 122A through the 3D VRenvironment 120, 120A, thereby providing a provenance of data (e.g., theuser feedback data 310) that can be used by the computing system 110 todynamically provide give new insights and annotations (e.g., the pathwayguidance 128A) for the user 140, and to improve training of the ML task322. In embodiments of the invention, the provenance of data tracked bythe display 400 documents the inputs, entities, systems, and processesthat influence the data of what the user 140 views, which in effectprovides a historical record of the data of what the user 140 views andits origins. In some embodiments of the invention, the provenance ofdata tracked by the display 400 includes a pathway delta, which is adifference, if any, between the pathway recommendation(s) 122, 122Apresented to the user 140 and the actual pathway the user 140 traversesin moving through the 3D VR environment 120, 120A. In embodiments of theinvention, the pathway delta can also be used by the computing system110 to dynamically provide give new insights and annotations (e.g., thepathway guidance 128A) for the user 140, and to improve training of theML task 322.

FIG. 5 depicts a computer-implemented methodology 500 in accordance withaspects of the invention. The methodology 500 can be implemented by thesystem 100 to generate the 3D VR environment 120, 120A having pathwayrecommendations 122, 122A in accordance with embodiments of theinvention. The methodology 500 begins at block 502 where the system 100receives the user query 314 from the user 140, along with seismic data312 (e.g., from the transducer system 112) and begins preparing theintermediate 3D VR environment 318 that is responsive to the user query314 but that does not include the pathway recommendations 122, 122A.Block 502 passes the intermediate 3D VR environment 318 to block 504 andblock 506. In block 506, the machine learning algorithms 320 have beentrained to perform the ML task 322 using training data 306 (e.g., userfeedback data 310 and/or historical target environment analysis corpus308) retrieved from databases at block 508. As previously describedherein, the ML task 322 generates ML task results 324 that includepredicted FOI within the intermediate 3D VR environment 318, as well aspredicted explanatory annotations of the predicted FOI. In accordancewith aspects of the invention, the FOI are the features of theintermediate 3D VR environment 318 that are relevant to and/or support asuccessful completion of the user inquiry/task 314.

In accordance with aspects of the invention, at block 504 the pathwayrecommender module 326 receives the intermediate 3D VR environment 318from block 502; receives the ML task results 324 from block 506;receives various multi-model data (e.g., user profile data) from thedatabases at block 508; and uses the outputs from blocks 502, 506, 508to generate the 3D VR environment 120, 120A having the pathwayrecommendation 122, 122A. More specifically, at block 504 the pathwayrecommender module 326 uses the FOI (part of the ML task results 324),the explanatory annotations of the FOI (part of the ML task results324), and other information (e.g., stored user profile information) togenerate the points of interest 124A, the pathway guidance 128A, and thePOI patterns 130A, all of which are used by the pathway recommender 326to generate the pathway recommendation 122, 122A; combine the pathwayrecommendation 122, 122A with the intermediate 3D VR environment 318;and output the 3D VR environment 120, 120A.

At block 510, the HMD 150, 150A receives the 3D VR environment 120, 120Aand uses the 3D VR environment 120, 120A to generate and present thedisplay 400 to the user 140. At block 512, the user 140 uses the pathwayrecommendations 122, 122A as a guide to navigating the 3D VR environment120, 122A. As previously noted herein, in embodiments of the invention,the display 400 of the HMD 150, 150A is configured and arranged to trackand record what the user 140 views while navigating the pathwayrecommendations 122 through the 3D VR environment 120, 120A, therebyproviding a provenance of data (e.g., the user feedback data 310) thatcan be used by the computing system 110 to dynamically provide give newinsights and annotations (e.g., the pathway guidance 128A) for the user140, and to improve training of the ML task 322. In embodiments of theinvention, the provenance of data tracked by the display 400 documentsthe inputs, entities, systems, and processes that influence the data ofwhat the user 140 views, which in effect provides a historical record ofthe data of what the user 140 views and its origins. In some embodimentsof the invention, the provenance of data tracked by the display 400includes a pathway delta, which is a difference, if any, between thepathway recommendation(s) 122, 122A presented to the user 140 and theactual pathway the user 140 traverses in moving through the 3D VRenvironment 120, 120A. In embodiments of the invention, the pathwaydelta can also be used by the computing system 110 to dynamicallyprovide give new insights and annotations (e.g., the pathway guidance128A) for the user 140, and to improve training of the ML algorithms 320to perform the ML task 322.

FIG. 6 depicts a block diagram illustrating an immersive VR system 600according to embodiments of the invention. In accordance with aspects ofthe invention, the immersive VR system 600 is a more detailed example ofhow portions of the immersive VR system 100 (shown in FIG. 1 ) can beimplemented. As shown in FIG. 6 , the system 600 includes a frontendmodule 610, a service module 620, and a datasources module 630. Thefrontend module 610 includes a system viewer module 612 and a systemingester module 614. The service module 620 includes a path recommendermodule 622, a query engine 624, and a ML models module 626. The datasources module 630 includes an expert profile module 632, a seismic datamodule 634, a semantic data module 636, and a provenance data module638.

Expert A interfaces with the frontend module 610, which includes thesystem ingester module 614 and the system viewer module 612, configuredto perform visualization operations and bootstrap operations.Visualization operations include the core functionality of the system600, including displaying the 3D VR environment(s) 120, 120A to ExpertA. Bootstrap operations include the loading and ingestion of varioustypes of data used by the system 600 from various sources. The frontendmodule 610 also receives and loads base input data (e.g., seismic datafiles, papers, annotations, and the like) from multiple sources. Thebase input data includes data that will be used by the system 600 togenerate the 3D VR environment(s) 120, 120A with pathwayrecommendation(s) 122, 122A. The system ingester module 614 of thefrontend module 610 is an administrative system configured and arrangedto input and manage data inputs for the entire system 600. Expert Ainterfaces with the frontend module 610 to input to the system viewermodule 612 a variety of user inputs, including user queries and/or userprofile information. Expert A also interfaces with the viewer systemmodule 612 to receive from the system viewer module 612 the 3D VRenvironments 120, 120A having the pathway recommendations 122, 122A. Inaccordance with aspects of the invention, Expert A corresponds to user140; the user queries and the user profile information correspond to theuser inquiry/task 314; and the system view module 612 corresponds to theHMD 150, 150A and the manipulation device(s) 152.

The services module 620 includes various services or functionality thatprocess and generate the pathway recommendations 122, 122A that will bedisplayed by the system viewer module 612. The machine learning modelmodule 626 can be used to identify or predict fields of interest (FOI)in the intermediate 3D VR environment 318. The query engine module 624executes queries against data in the datasources module 630 to provideresponses/answers that will be used by the machine learning modelmodules 626 and the path recommender module 622. The path recommendermodule 622 joins data from multiple sources and solves an optimizationproblem to generate the pathway recommendation(s) 122, 122A. Servicesmodule 620 corresponds to the functionality of the computing system 110depicted in FIG. 3C; the machine learning model modules 626 correspondsto the ML algorithms 320 shown in FIG. 3C; and the pathway recommendermodule 622 corresponds to the pathway recommender module 326 shown inFIG. 3C.

The datasources module 630 can be implemented as a multimodal databasedesigned to support multiple data models against a single, integratedbackend. Document, graph, relational, and key-value models are examplesof data models that can be supported by the multimodal databaseimplementation of the datasources module 630. In accordance with aspectsof the invention, the various modules that make up the datasourcesmodule 630 include data/information gathered in real time forimplementation of a specific user inquiry/task 314 (e.g., userinquiry/task 314, seismic analysis data 312, and user feedback data310), along with historical data (e.g., historical target environmentanalysis corpus 308) that has been gathered from a variety of sourcesand stored for a variety of uses, including but not limited to trainingthe machine learning model modules 626. The expert profile data module632 corresponds to the previously-described stored profile data of theuser 140; the seismic data module 634 corresponds to the seismicanalysis data 312 and/or the historical target environment analysiscorpus 308; the semantic data module 636 corresponds to any and allnatural language data utilized by the system 600, including but notlimited to information provided in the pathway guidance 128A; and theprovenance data module 638 corresponds to user feedback data 310 shownin FIG. 3C.

In embodiments of the invention, Expert B represents and corresponds tothe users from which the historical portions of the datasources module630 are derived. In embodiments of the invention, the historicalportions of the datasources module 630 represents and corresponds to thehistorical target environment analysis corpus 308 shown in FIG. 3C. Inembodiments of the invention, the machine learning model module 626 canbe trained in substantially the same manner as the ML algorithms 320shown in FIG. 3C and the ML classifier system 900 shown in FIG. 9 .

FIG. 7 depicts a flow diagram illustrating a computer-implementedmethodology 700 according to embodiments of the invention. Themethodology 700 can be performed by the system 600. FIGS. 8A-8E depictblock diagrams illustrating results of a path recommender algorithmrepresented by operations performed at blocks 707, 708 709 of thecomputer-implemented methodology 700. FIGS. 8A-8E depict a personalizedpathway recommendation 122B at various stages at it is being constructedby the operations performed at block 707, 708, 709 of thecomputer-implemented methodology 700. The following description of themethodology 700 will make reference to the flow diagram depicted in FIG.7 , along with the block diagrams depicted in FIGS. 8A-8E whereappropriate.

At block 701, the system 600 receives the input which could be a taskthat is defined as a query or a structured profile of Expert A. Atdecision block 702, if the input requires semantic/spatial reasoning,the pathway recommender module 622 prepare a query to submit to queryengine module 624. At block 703, the query engine module 624 fetchesdata from multiple sources in the datasources module 630, including forexample semantic data from the semantic data module 632 that can be usedto execute reasoning over labeled data along the profile preferences tofilter relevant results; and/or calculate spatial operations to findinteresting setup of structure with important spatial relationships. Atdecision block 702, if the input does not require semantic/spatialreasoning, the method moves to decision block 704. At block 704, if theinput requires machine learning models and those are available to thesystem 600, block 704 will submit the input to the machine learningmodel module 626 for processing. At block 705, the machine learningmodel module 626 receives the parameters and seismic data to segmentfeatures of interest (FOI) in batches and output the FOI back to thepath recommender module 622. At block 706, the path recommender module622 gathers all ROI's from the block 705 to prepare them to beincorporated into the path generation operations to be performed by thepathway recommender module 622.

Blocks 707-709 represent operations performed by a pathway recommenderalgorithm of the pathway recommender module 622. At block 707, and asdepicted in FIGS. 8A-8C, the pathway recommender module 626 clustersROIs that are near or have spatial dependency that makes it appropriatefor them to be inspected in the same view, which in turn will be in thesame room. The pathway recommender module 622 splits ROI's into twotypes, the reference points (RP) and the focus points (FP), which arethe points to be studied, and the points that Expert A will face. TheRPs are objects that Expert A must gain a spatial relationships of inorder to understand a specific FP better. The pathway recommender module622 classifies the RP into two groups, the ones that should always bevisible (RPV) from the FP and the ones that Expert A should view duringthe walk (RPW). The pathway recommender module 622 clusters the RP ofeach FP by distance, i.e., RPs that are closer than a threshold and arerelated for the same FP, are considered one. Then the pathwayrecommender module 622 creates a cave room for each cluster.

At block 708, and as depicted in FIG. 8D, the pathway recommender module622 creates a graph in which each cluster is a node, and the edges havedistance and/or the relevance among the nodes based on weights appliedto the edges. The rooms are generated for the geological features bycalculating the bound volumes of them. The rooms are connected bycreating a tunnel or corridor shape geometry by interpolating theboundaries of the room.

At block 709, and as depicted in FIG. 8E, the pathway recommender module622 generates a recommended pathway by traversing the graph followingshortest path criteria or relevance between clusters. At block 710, thepathway recommender module 622 synthesizes the geometry of therecommended pathway.

Aspects of the invention can be applied to a variety of types of targetenvironment 130. For example, embodiments of the invention can be usedto perform fluid analysis and characterization tasks where the targetenvironment 130 is a reservoir, and the 3D VR environment 120, 122A is asimulation of the reservoir. In the 3D VR environment 120, reservoirdata are represented by 3D cells forming volumetric data and 4D data aswell, wherein the value of each cell can change over time. Expert A canbe a reservoir engineers interested in visualizing estimated physicalproperties generated by simulations in a reservoir. For example,engineers may simulation the arrangement for wells and estimate the mostefficient setup for oil or gas exploration. The pathway recommendermodule 622 can analyze the results of a simulation and suggest a pathwith point of view highlighting changes on key regions and wellslocations. For four-dimensional (4D) simulations, it can be moredifficult to follow the changes, and embodiments of the invention cancontribute to improving Expert A's comprehension of such cases.

As another example, embodiments of the invention can be used to performimmersive analysis over a computerized tomography (CT) scan. A CT scancombines a series of X-ray images taken from different angles around thebody and uses computer processing to create cross-sectional images(slices) of the bones, blood vessels and soft tissues inside your body.CT scan images provide more-detailed information than plain X-rays do.The several slices of a CT scan can be combined to generate a 3D volumeanalogous to the 3D VR environment 120A generated for seismic data.Using visualization techniques, the CT scan 3D volume can be visualizedusing transfer functions that set opacity of voxels and allows users tovisualize internal structures of the data. Embodiments of the inventioncan generate the pathway recommendation 122 for the CT scan 3D volumethat allow the user 140 to virtually navigate the human body to assistwith image diagnostic operations or with review and development of asurgery plan.

Additional details of machine learning techniques that can be used toaspects of the invention disclosed herein will now be provided. Thevarious types of computer control functionality of the processorsdescribed herein can be implemented using machine learning and/ornatural language processing techniques. In general, machine learningtechniques are run on so-called “neural networks,” which can beimplemented as programmable computers configured to run sets of machinelearning algorithms and/or natural language processing algorithms.Neural networks incorporate knowledge from a variety of disciplines,including neurophysiology, cognitive science/psychology, physics(statistical mechanics), control theory, computer science, artificialintelligence, statistics/mathematics, pattern recognition, computervision, parallel processing and hardware (e.g.,digital/analog/VLSI/optical).

The basic function of neural networks and their machine learningalgorithms is to recognize patterns by interpreting unstructured sensordata through a kind of machine perception. Unstructured real-world datain its native form (e.g., images, sound, text, or time series data) isconverted to a numerical form (e.g., a vector having magnitude anddirection) that can be understood and manipulated by a computer. Themachine learning algorithm performs multiple iterations oflearning-based analysis on the real-world data vectors until patterns(or relationships) contained in the real-world data vectors areuncovered and learned. The learned patterns/relationships function aspredictive models that can be used to perform a variety of tasks,including, for example, classification (or labeling) of real-world dataand clustering of real-world data. Classification tasks often depend onthe use of labeled datasets to train the neural network (i.e., themodel) to recognize the correlation between labels and data. This isknown as supervised learning. Examples of classification tasks includeidentifying objects in images (e.g., stop signs, pedestrians, lanemarkers, etc.), recognizing gestures in video, detecting voices,detecting voices in audio, transcribing speech into text, and the like.Clustering tasks identify similarities between objects, which it groupsaccording to those characteristics in common and which differentiatethem from other groups of objects. These groups are known as “clusters.”

An example of machine learning techniques that can be used to implementaspects of the invention will be described with reference to FIGS. 9 and10 . Machine learning models configured and arranged according toembodiments of the invention will be described with reference to FIG. 9. Detailed descriptions of an example computing system and networkarchitecture capable of implementing one or more of the embodiments ofthe invention described herein will be provided with reference to FIG.11 .

FIG. 9 depicts a block diagram showing a machine learning or classifiersystem 900 capable of implementing various aspects of the inventiondescribed herein. More specifically, the functionality of the system 900is used in embodiments of the invention to generate various models andsub-models that can be used to implement computer functionality inembodiments of the invention. The system 900 includes multiple datasources 902 in communication through a network 904 with a classifier910. In some aspects of the invention, the data sources 902 can bypassthe network 904 and feed directly into the classifier 910. The datasources 902 provide data/information inputs that will be evaluated bythe classifier 910 in accordance with embodiments of the invention. Thedata sources 902 also provide data/information inputs that can be usedby the classifier 910 to train and/or update model(s) 916 created by theclassifier 910. The data sources 902 can be implemented as a widevariety of data sources, including but not limited to, sensorsconfigured to gather real time data, data repositories (includingtraining data repositories), and outputs from other classifiers. Thenetwork 904 can be any type of communications network, including but notlimited to local networks, wide area networks, private networks, theInternet, and the like.

The classifier 910 can be implemented as algorithms executed by aprogrammable computer such as a processing system 1100 (shown in FIG. 11). As shown in FIG. 9 , the classifier 910 includes a suite of machinelearning (ML) algorithms 912; natural language processing (NLP)algorithms 914; and model(s) 916 that are relationship (or prediction)algorithms generated (or learned) by the ML algorithms 912. Thealgorithms 912, 914, 916 of the classifier 910 are depicted separatelyfor ease of illustration and explanation. In embodiments of theinvention, the functions performed by the various algorithms 912, 914,916 of the classifier 910 can be distributed differently than shown. Forexample, where the classifier 910 is configured to perform an overalltask having sub-tasks, the suite of ML algorithms 912 can be segmentedsuch that a portion of the ML algorithms 912 executes each sub-task anda portion of the ML algorithms 912 executes the overall task.Additionally, in some embodiments of the invention, the NLP algorithms914 can be integrated within the ML algorithms 912.

The NLP algorithms 914 include speech recognition functionality thatallows the classifier 910, and more specifically the ML algorithms 912,to receive natural language data (text and audio) and apply elements oflanguage processing, information retrieval, and machine learning toderive meaning from the natural language inputs and potentially takeaction based on the derived meaning. The NLP algorithms 914 used inaccordance with aspects of the invention can also include speechsynthesis functionality that allows the classifier 910 to translate theresult(s) 920 into natural language (text and audio) to communicateaspects of the result(s) 920 as natural language communications.

The NLP and ML algorithms 914, 912 receive and evaluate input data(i.e., training data and data-under-analysis) from the data sources 902.The ML algorithms 912 includes functionality that is necessary tointerpret and utilize the input data's format. For example, where thedata sources 902 include image data, the ML algorithms 912 can includevisual recognition software configured to interpret image data. The MLalgorithms 912 apply machine learning techniques to received trainingdata (e.g., data received from one or more of the data sources 902) inorder to, over time, create/train/update one or more models 916 thatmodel the overall task and the sub-tasks that the classifier 910 isdesigned to complete.

Referring now to FIGS. 9 and 10 collectively, FIG. 10 depicts an exampleof a learning phase 1000 performed by the ML algorithms 912 to generatethe above-described models 916. In the learning phase 1000, theclassifier 910 extracts features from the training data and coverts thefeatures to vector representations that can be recognized and analyzedby the ML algorithms 912. The features vectors are analyzed by the MLalgorithm 912 to “classify” the training data against the target model(or the model's task) and uncover relationships between and among theclassified training data. Examples of suitable implementations of the MLalgorithms 912 include but are not limited to neural networks, supportvector machines (SVMs), logistic regression, decision trees, hiddenMarkov Models (HMIs), etc. The learning or training performed by the MLalgorithms 912 can be supervised, unsupervised, or a hybrid thatincludes aspects of supervised and unsupervised learning. Supervisedlearning is when training data is already available andclassified/labeled. Unsupervised learning is when training data is notclassified/labeled so must be developed through iterations of theclassifier 910 and the ML algorithms 912. Unsupervised learning canutilize additional learning/training methods including, for example,clustering, anomaly detection, neural networks, deep learning, and thelike.

When the models 916 are sufficiently trained by the ML algorithms 912,the data sources 902 that generate “real world” data are accessed, andthe “real world” data is applied to the models 916 to generate usableversions of the results 920. In some embodiments of the invention, theresults 920 can be fed back to the classifier 910 and used by the MLalgorithms 912 as additional training data for updating and/or refiningthe models 916.

In aspects of the invention, the ML algorithms 912 and the models 916can be configured to apply confidence levels (CLs) to various ones oftheir results/determinations (including the results 920) in order toimprove the overall accuracy of the particular result/determination.When the ML algorithms 912 and/or the models 916 make a determination orgenerate a result for which the value of CL is below a predeterminedthreshold (TH) (i.e., CL<TH), the result/determination can be classifiedas having sufficiently low “confidence” to justify a conclusion that thedetermination/result is not valid, and this conclusion can be used todetermine when, how, and/or if the determinations/results are handled indownstream processing. If CL>TH, the determination/result can beconsidered valid, and this conclusion can be used to determine when,how, and/or if the determinations/results are handled in downstreamprocessing. Many different predetermined TH levels can be provided. Thedeterminations/results with CL>TH can be ranked from the highest CL>THto the lowest CL>TH in order to prioritize when, how, and/or if thedeterminations/results are handled in downstream processing.

In aspects of the invention, the classifier 910 can be configured toapply confidence levels (CLs) to the results 920. When the classifier910 determines that a CL in the results 920 is below a predeterminedthreshold (TH) (i.e., CL<TH), the results 920 can be classified assufficiently low to justify a classification of “no confidence” in theresults 920. If CL>TH, the results 920 can be classified as sufficientlyhigh to justify a determination that the results 920 are valid. Manydifferent predetermined TH levels can be provided such that the results920 with CL>TH can be ranked from the highest CL>TH to the lowest CL>TH.

The functions performed by the classifier 910, and more specifically bythe ML algorithm 912, can be organized as a weighted directed graph,wherein the nodes are artificial neurons (e.g. modeled after neurons ofthe human brain), and wherein weighted directed edges connect the nodes.The directed graph of the classifier 910 can be organized such thatcertain nodes form input layer nodes, certain nodes form hidden layernodes, and certain nodes form output layer nodes. The input layer nodescouple to the hidden layer nodes, which couple to the output layernodes. Each node is connected to every node in the adjacent layer byconnection pathways, which can be depicted as directional arrows thateach has a connection strength. Multiple input layers, multiple hiddenlayers, and multiple output layers can be provided. When multiple hiddenlayers are provided, the classifier 910 can perform unsuperviseddeep-learning for executing the assigned task(s) of the classifier 910.

Similar to the functionality of a human brain, each input layer nodereceives inputs with no connection strength adjustments and no nodesummations. Each hidden layer node receives its inputs from all inputlayer nodes according to the connection strengths associated with therelevant connection pathways. A similar connection strengthmultiplication and node summation is performed for the hidden layernodes and the output layer nodes.

The weighted directed graph of the classifier 910 processes data records(e.g., outputs from the data sources 902) one at a time, and it “learns”by comparing an initially arbitrary classification of the record withthe known actual classification of the record. Using a trainingmethodology knows as “back-propagation” (i.e., “backward propagation oferrors”), the errors from the initial classification of the first recordare fed back into the weighted directed graphs of the classifier 910 andused to modify the weighted directed graph's weighted connections thesecond time around, and this feedback process continues for manyiterations. In the training phase of a weighted directed graph of theclassifier 910, the correct classification for each record is known, andthe output nodes can therefore be assigned “correct” values. Forexample, a node value of “1” (or 0.9) for the node corresponding to thecorrect class, and a node value of “0” (or 0.1) for the others. It isthus possible to compare the weighted directed graph's calculated valuesfor the output nodes to these “correct” values, and to calculate anerror term for each node (i.e., the “delta” rule). These error terms arethen used to adjust the weights in the hidden layers so that in the nextiteration the output values will be closer to the “correct” values.

FIG. 11 depicts a high level block diagram of the computer system 1100,which can be used to implement one or more computer processingoperations in accordance with aspects of the present invention. Althoughone exemplary computer system 1100 is shown, computer system 1100includes a communication path 1125, which connects computer system 1100to additional systems (not depicted) and can include one or more widearea networks (WANs) and/or local area networks (LANs) such as theInternet, intranet(s), and/or wireless communication network(s).Computer system 1100 and the additional systems are in communication viacommunication path 1125, e.g., to communicate data between them.

Computer system 1100 includes one or more processors, such as processor1102. Processor 1102 is connected to a communication infrastructure 1104(e.g., a communications bus, cross-over bar, or network). Computersystem 1100 can include a display interface 1106 that forwards graphics,text, and other data from communication infrastructure 1104 (or from aframe buffer not shown) for display on a display unit 1108. Computersystem 1100 also includes a main memory 1110, preferably random accessmemory (RAM), and can also include a secondary memory 1112. Secondarymemory 1112 can include, for example, a hard disk drive 1114 and/or aremovable storage drive 1116, representing, for example, a floppy diskdrive, a magnetic tape drive, or an optical disk drive. Removablestorage drive 1116 reads from and/or writes to a removable storage unit1118 in a manner well known to those having ordinary skill in the art.Removable storage unit 1118 represents, for example, a floppy disk, acompact disc, a magnetic tape, or an optical disk, flash drive, solidstate memory, etc. which is read by and written to by removable storagedrive 1116. As will be appreciated, removable storage unit 1118 includesa computer readable medium having stored therein computer softwareand/or data.

In alternative embodiments of the invention, secondary memory 1112 caninclude other similar means for allowing computer programs or otherinstructions to be loaded into the computer system. Such means caninclude, for example, a removable storage unit 1120 and an interface1122. Examples of such means can include a program package and packageinterface (such as that found in video game devices), a removable memorychip (such as an EPROM, or PROM) and associated socket, and otherremovable storage units 1120 and interfaces 1122 which allow softwareand data to be transferred from the removable storage unit 1120 tocomputer system 1100.

Computer system 1100 can also include a communications interface 1124.Communications interface 1124 allows software and data to be transferredbetween the computer system and external devices. Examples ofcommunications interface 1124 can include a modem, a network interface(such as an Ethernet card), a communications port, or a PCM-CIA slot andcard, etcetera. Software and data transferred via communicationsinterface 1124 are in the form of signals which can be, for example,electronic, electromagnetic, optical, or other signals capable of beingreceived by communications interface 1124. These signals are provided tocommunications interface 1124 via communication path (i.e., channel)1125. Communication path 1125 carries signals and can be implementedusing wire or cable, fiber optics, a phone line, a cellular phone link,an RF link, and/or other communications channels.

The following definitions and abbreviations are to be used for theinterpretation of the claims and the specification. As used herein, theterms “comprises,” “comprising,” “includes,” “including,” “has,”“having,” “contains” or “containing,” or any other variation thereof,are intended to cover a non-exclusive inclusion. For example, acomposition, a mixture, a process, a method, an article, or an apparatusthat comprises a list of elements is not necessarily limited to onlythose elements but can include other elements not expressly listed orinherent to such composition, mixture, process, method, article, orapparatus.

The terminology used herein is for the purpose of describing particularembodiments of the invention only and is not intended to be limiting ofthe present invention. As used herein, the singular forms “a”, “an” and“the” are intended to include the plural forms as well, unless thecontext clearly indicates otherwise. It will be further understood thatthe terms “comprises” and/or “comprising,” when used in thisspecification, specify the presence of stated features, integers, steps,operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, element components, and/or groups thereof.

Additionally, the term “exemplary” and variations thereof are usedherein to mean “serving as an example, instance or illustration.” Anyembodiment or design described herein as “exemplary” is not necessarilyto be construed as preferred or advantageous over other embodiments ordesigns. The terms “at least one,” “one or more,” and variationsthereof, can include any integer number greater than or equal to one,i.e. one, two, three, four, etc. The terms “a plurality” and variationsthereof can include any integer number greater than or equal to two,i.e., two, three, four, five, etc. The term “connection” and variationsthereof can include both an indirect “connection” and a direct“connection.”

The terms “about,” “substantially,” “approximately,” and variationsthereof, are intended to include the degree of error associated withmeasurement of the particular quantity based upon the equipmentavailable at the time of filing the application. For example, “about”can include a range of ±8% or 5%, or 2% of a given value.

The phrases “in signal communication”, “in communication with,”“communicatively coupled to,” and variations thereof can be usedinterchangeably herein and can refer to any coupling, connection, orinteraction using electrical signals to exchange information or data,using any system, hardware, software, protocol, or format, regardless ofwhether the exchange occurs wirelessly or over a wired connection.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the presentinvention. As used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,element components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

It will be understood that those skilled in the art, both now and in thefuture, may make various improvements and enhancements which fall withinthe scope of the claims which follow.

What is claimed is:
 1. A computer-implemented method of generating apathway recommendation, the computer-implemented method comprising:using a processor system to generate an intermediate three-dimensional(3D) virtual reality (VR) environment of a target environment; using amachine learning algorithm to perform a machine learning task on theintermediate 3D VR environment to generate machine learning task resultscomprising predicted features of interest (FOI) and FOI annotations forthe intermediate 3D VR environment; using the processor system togenerate, based at least in part on the machine learning task results,the pathway recommendation: generating an immersive 3D VR environmentcomprising the intermediate 3D VR environment integrated with thepathway recommendation; and presenting the immersive 3D VR environmentto a user such that the pathway recommendation of the immersive 3D VRenvironment operates as a recommendation for the user to follow thepathway recommendation.
 2. The computer-implemented method of claim 1,wherein: the recommendation for the user to follow the pathwayrecommendation assists the user with navigating and interpreting theimmersive 3D VR environment comprising the intermediate 3D VRenvironment integrated with the pathway recommendation; and theprocessor system enables the user to not follow the pathwayrecommendation and navigate through a portion of the immersive 3D VRenvironment that is outside the pathway recommendation.
 3. Thecomputer-implemented method of claim 1 further comprising using theprocessor system to generate the pathway recommendation based at leastin part on: a user profile data; and a user task to be performed by theuser on the immersive 3D VR environment.
 4. The computer-implementedmethod of claim 1 further comprising using the processor system togenerate, based at least in part on user feedback data, the pathwayrecommendation, wherein the user feedback data comprises feedback theprocessor system receives from the user about the pathway recommendationwhile the user is being guided through the immersive 3D VR environmentby the pathway recommendation.
 5. The computer-implemented method ofclaim 1, wherein the pathway recommendation comprises pathway guidancecomprising a communication generated by the processor system andprovided to the user to guide the user through the pathwayrecommendation.
 6. The computer-implemented method of claim 1, whereinthe pathway recommendation comprises a point-of-interest (POI) patterncomprising a grouping of points of interest of the pathwayrecommendation, wherein the grouping conveys information to the userthat is relevant to the user task.
 7. The computer-implemented method ofclaim 1, wherein the machine learning algorithm has been trained toperform the machine learning task using a historical target environmentanalysis corpus comprising information from prior analyses performed bytrained interpreters on other target environments.
 8. A computer systemcomprising a processor communicatively coupled to a memory, wherein theprocessor performs processor operations comprising: generating anintermediate three-dimensional (3D) virtual reality (VR) environment ofa target environment; using a machine learning algorithm to perform amachine learning task on the intermediate 3D VR environment to generatemachine learning task results comprising predicted features of interest(FOI) and FOI annotations for the intermediate 3D VR environment;generating, based at least in part on the machine learning task results,a pathway recommendation; generating an immersive 3D VR environmentcomprising the intermediate 3D VR environment integrated with thepathway recommendation; and presenting the immersive 3D VR environmentto a user such that the pathway recommendation of the immersive 3Denvironment operates as a recommendation for the user to follow thepathway recommendation.
 9. The computer system of claim 8, wherein: therecommendation for the user to follow the pathway recommendation assiststhe user with navigating and interpreting the immersive 3D VRenvironment comprising the intermediate 3D VR environment integratedwith the pathway recommendation; and the processor operations includeenabling the user to not follow the pathway recommendation and navigatethrough a portion of the immersive 3D VR environment that is outside thepathway recommendation.
 10. The computer system of claim 8, wherein theprocessor operations further comprise generating the pathwayrecommendation based at least in part on: a user profile data; and auser task to be performed by the user on the immersive 3D VRenvironment.
 11. The computer system of claim 8, wherein the processoroperations further comprise generating, based at least in part on userfeedback data, the pathway recommendation, wherein the user feedbackdata comprises feedback the processor system receives from the userabout the pathway recommendation while the user is being guided throughthe immersive 3D VR environment by the pathway recommendation.
 12. Thecomputer system of claim 8, wherein the pathway recommendation comprisespathway guidance comprising a communication generated by the processorsystem and provided to the user to guide the user through the pathwayrecommendation.
 13. The computer system of claim 8, wherein the pathwayrecommendation comprises a point-of-interest (POI) pattern comprising agrouping of points of interest of the pathway recommendation, whereinthe grouping conveys information to the user that is relevant to theuser task.
 14. The computer system of claim 8, wherein the machinelearning algorithm has been trained to perform the machine learning taskusing a historical target environment analysis corpus comprisinginformation from prior analyses performed by trained interpreters onother target environments.
 15. A computer program product for generatinga pathway recommendation, the computer program product comprising acomputer readable program stored on a computer readable storage medium,wherein the computer readable storage medium is not a transitory signalper se, wherein the computer readable program, when executed on aprocessor system, causes the processor system to perform a processoroperations comprising: generating an intermediate three-dimensional (3D)virtual reality (VR) environment of a target environment; using amachine learning algorithm to perform a machine learning task on theintermediate 3D VR environment to generate machine learning task resultscomprising predicted features of interest (FOI) and FOI annotations forthe intermediate 3D VR environment; generating, based at least in parton the machine learning task results, a pathway recommendation;generating an immersive 3D VR environment comprising the intermediate 3DVR environment integrated with the pathway recommendation; andpresenting the immersive 3D VR environment to a user such that thepathway recommendation of the 3D VR environment operates as arecommendation for the user to follow the pathway recommendation. 16.The computer program product of claim 15, wherein: the recommendationfor the user to follow the pathway recommendation assists the user withnavigating and interpreting the immersive 3D VR environment comprisingthe intermediate 3D VR environment integrated with the pathwayrecommendation; and the processor operations include enabling the userto not follow the pathway recommendation and navigate through a portionof the immersive 3D VR environment that is outside the pathwayrecommendation.
 17. The computer program product of claim 15, whereinthe processor operations further comprise generating the pathwayrecommendation based at least in part on: a user profile data; and auser task to be performed by the user on the immersive 3D VRenvironment.
 18. The computer program product of claim 15, wherein theprocessor operations further comprise generating, based at least in parton user feedback data, the pathway recommendation, wherein the userfeedback data comprises feedback the processor system receives from theuser about the pathway recommendation while the user is being guidedthrough the immersive 3D VR environment by the pathway recommendation.19. The computer program product of claim 15, wherein the pathwayrecommendation comprises: pathway guidance comprising a communicationgenerated by the processor system and provided to the user to guide theuser through the pathway recommendation; and a point-of-interest (POI)pattern comprising a grouping of points of interest of the pathwayrecommendation, wherein the grouping conveys information to the userthat is relevant to the user task.
 20. The computer program product ofclaim 15, wherein the machine learning algorithm has been trained toperform the machine learning task using a historical target environmentanalysis corpus comprising information from prior analyses performed bytrained interpreters on other target environments.